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Primer of Biostatistics: Sixth Edition

ISBN: 9780071435093 | 0071435093
Edition: 6th
Format: Paperback
Publisher: McGraw-Hill Medical
Pub. Date: 4/15/2005

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SummaryTable of ContentsAuthor Biography
Extremely popular, this student-friendly text presents the practical areas of statistics in terms of their relevance to medicine and the life sciences. Includes many illustrative examples and challenging problems that reinforce the author's unique and intuitive approach to the subject. The new edition features a new two-color design, examples taken from current biomedical literature, and review questions within each chapter.
Focuses on practical areas of statistics in terms of their relevance to biomedical applica... MORE
Location of Tables for Tests of Significancexv
Prefacexvii
Biostatistics and Clinical Practice
1(10)
``Scientific'' Medicine
... MORE		2(3)
What Do Statistical Procedures Tell You?
5(2)
Why Not Depend on the Journals?
7(2)
Why Has the Problem Persisted?
9(2)
How to Summarize Data
11(29)
The Mean
13(1)
Measures of Variability
14(1)
The Normal Distribution
15(1)
Percentiles
16(5)
Getting the Data
21(9)
Random Sampling
21(3)
Bias
24(2)
Experiments and Observational Studies
26(3)
Randomized Clinical Trials
29(1)
How to Estimate the Mean and Standard Deviation from a Sample
30(1)
How Good Are These Estimates?
31(7)
Summary
38(1)
Problems
38(2)
How to Test for Differences between Groups
40(33)
The General Approach
41(5)
Two Different Estimates of the Population Variance
46(2)
What Is a ``Big'' F?
48(8)
Three Examples
56(11)
Glucose Levels in Children of Parents with Diabetes
56(4)
Halothane versus Morphine for Open-Heart Surgery
60(4)
Menstrual Dysfunction in Distance Runners
64(3)
Problems
67(6)
The Special Case of Two Groups: The t Test
73(53)
The General Approach
75(2)
The Standard Deviation of a Difference or a Sum
77(3)
Use of t to Test Hypotheses about Two Groups
80(7)
What If the Two Samples Are Not the Same Size?
87(1)
The Examples Revisited
88(4)
Glucose Levels in Children of Parents with Diabetes
88(1)
Halothane versus Morphine for Open-Heart Surgery
88(4)
The t Test Is an Analysis of Variance
92(3)
Common Errors in the Use of the t Test and How to Compensate for Them
95(3)
How to Use t Tests to Isolate Differences between Groups in Analysis of Variance
98(8)
The Bonferroni t Test
98(2)
More on Menstruation and Jogging
100(1)
A Better Approach to Multiple Comparisons: The Holm t Test
101(3)
The Holm-Sidak Test
104(2)
Other Approaches to Multiple Comparison Testing: The Student-Newman-Keuls Test
106(5)
Still More on Menstruation and Jogging
107(3)
Tukey Test
110(1)
Which Multiple Comparison Procedure Should You Use?
111(1)
Multiple Comparisons against a Single Control
112(5)
Bonferroni t Test
112(1)
Holm t Test
113(1)
Dunnett's Test
113(4)
The Meaning of P
117(6)
Statistical versus Real (Clinical) Thinking
119(2)
Why P <.05?
121(2)
Problems
123(3)
How to Analyze Rates and Proportions
126(53)
Back to Mars
127(5)
Estimating Proportions from Samples
132(5)
Hypothesis Tests for Proportions
137(8)
The Yates Correction for Continuity
139(1)
Mortality Associated with Anesthesia for Open-Heart Surgery with Halothane or Morphine
140(2)
Prevention of Thrombosis in People Receiving Hemodialysis
142(3)
Another Approach to Testing Nominal Data: Analysis of Contingency Tables
145(7)
The Chi-Square Test Statistic
148(4)
Chi-Square Applications to Experiments with More Than Two Treatments or Outcomes
152(6)
Subdividing Contingency Tables
155(3)
The Fisher Exact Test
158(5)
Measures of Association Between Two Nominal Variables
163(7)
Prospective Studies and Relative Risk
164(2)
Case-Control Studies and the Odds Ratio
166(2)
Passive Smoking and Breast Cancer
168(2)
Problems
170(9)
What Does ``Not Significant'' Really Mean?
179(40)
An Effective Diuretic
180(5)
Two Types of Errors
185(1)
What Determines a Test's Power?
186(18)
The Size of the Type I Error α
187(6)
The Size of the Treatment Effect
193(2)
The Population Variability
195(2)
Bigger Samples Mean More Powerful Tests
197(4)
What Determines Power? A Summary
201(2)
Another Look at Halothane versus Morphine for Open-Heart Surgery
203(1)
Power and Sample Size for Analysis of Variance
204(4)
Power, Menstruation, and Running
206(2)
Power and Sample Size for Comparing Two Proportions
208(3)
Mortality Associated with Anesthesia for Open-Heart Surgery
210(1)
Sample Size for Comparing Two Proportions
211(1)
Power and Sample Size for Relative Risk and Odds Ratio
211(1)
Power and Sample Size for Contingency Tables
212(2)
Physicians, Perspiration, and Power
213(1)
Practical Problems in Using Power
214(1)
What Difference Does It Make?
215(3)
Problems
218(1)
Confidence Intervals
219(34)
The Size of the Treatment Effect Measured as the Difference of Two Means
220(3)
The Effective Diuretic
223(4)
More Experiments
224(3)
What Does ``Confidence'' Mean?
227(2)
Confidence Intervals Can Be Used to Test Hypotheses
229(2)
Confidence Interval for the Population Mean
231(2)
The Size of the Treatment Effect Measured as the Difference of Two Rates or Proportions
233(7)
Difference in Mortality Associated with Anesthesia for Open-Heart Surgery
234(1)
Difference in Thrombosis with Aspirin in People Receiving Hemodialysis
235(1)
How Negative Is a ``Negative'' Clinical Trial?
236(1)
Meta-analysis
236(4)
Confidence Interval for Rates and Proportions
240(5)
Quality of Evidence Used as a Basis for Interventions to Improve Hospital Antibiotic Prescribing
241(1)
Exact Confidence Intervals for Rates and Proportions
242(3)
Confidence Intervals for Relative Risk and Odds Ratio
245(2)
Difference in Thrombosis with Aspirin in People Receiving Hemodialysis
246(1)
Passive Smoking and Breast Cancer
247(1)
Confidence Interval for the Entire Population
247(4)
Problems
251(2)
How to Test for Trends
253(68)
More about the Martians
254(5)
The Population Parameters
257(2)
How to Estimate the Trend from a Sample
259(19)
The Best Straight Line through the Data
259(8)
Variability about the Regression Line
267(1)
Standard Errors of the Regression Coefficients
268(4)
How Convincing Is the Trend?
272(2)
Confidence Interval for the Line of Means
274(1)
Confidence Interval for an Observation
275(3)
How to Compare Two Regression Lines
278(7)
Overall Test for Coincidence of Two Regression Lines
280(1)
Relationship between Weakness and Muscle Wasting in Rheumatoid Arthritis
281(4)
Correlation and Correlation Coefficients
285(11)
The Pearson Product-Moment Correlation Coefficient
287(3)
The Relationship between Regression and Correlation
290(2)
How to Test Hypotheses about Correlation Coefficients
292(1)
Journal Size and Selectivity
293(3)
The Spearman Rank Correlation Coefficient
296(6)
Variation among Interns in Use of Laboratory Tests: Relation to Quality of Care
300(2)
Power and Sample Size in Regression and Correlation
302(3)
Comparing Two Different Measurements of the Same Thing: The Bland-Altman Method
305(5)
Assessing Mitral Regurgitation with Echocardiography
306(4)
Summary
310(1)
Problems
310(11)
Experiments When Each Subject Receives More than One Treatment
321(42)
Experiments When Subjects Are Observed before and after a Single Treatment: The Paired t Test
322(8)
Cigarette Smoking and Platelet Function
325(5)
Another Approach to Analysis of Variance
330(12)
Some New Notation
331(8)
Accounting for All the Variability in the Observations
339(3)
Experiments When Subjects Are Observed after Many Treatments: Repeated-Measures Analysis of Variance
342(12)
Anti-Asthmatic Drugs and Endotoxin
348(4)
How to Isolate Differences in Repeated-Measures Analysis of Variance
352(1)
Power in Repeated-Measures Analysis of Variance
353(1)
Experiments When Outcomes Are Measured on a Nominal Scale: McNemar's Test
354(3)
p7 Antigen Expression in Human Breast Cancer
354(3)
Problems
357(6)
Alternatives to Analysis of Variance and the t Test Based on Ranks
363(50)
How to Choose between Parametric and Nonparametric Methods
364(3)
Two Different Samples: The Mann-Whitney Rank-Sum Test
367(11)
The Leboyer Approach to Childbirth
374(4)
Each Subject Observed before and after One Treatment: The Wilcoxon Signed-Rank Test
378(8)
Cigarette Smoking and Platelet Function
385(1)
Experiments with Three or More Groups When Each Group Contains Different Individuals: The Kruskal-Wallis Statistic
386(9)
Prenatal Marijuana Exposure and Child Behavior
389(1)
Nonparametric Multiple Comparisons
390(2)
More on Marijuana
392(3)
Experiments in Which Each Subject Receives More than One Treatment: The Friedman Test
395(10)
Anti-Asthmatic Drugs and Endotoxin
399(2)
Multiple Comparisons after Friedman's Test
401(1)
Effect of Secondhand Smoke on Angina Pectoris
401(4)
Summary
405(1)
Problems
406(7)
How to Analyze Survival Data
413(31)
Censoring on Pluto
414(3)
Estimating the Survival Curve
417(10)
Median Survival Time
423(1)
Standard Errors and Confidence Limits for the Survival Curve
424(3)
Comparing Two Survival Curves
427(10)
Bone Marrow Transplantation to Treat Adult Leukemia
429(7)
The Yates Correction for the Log-Rank Test
436(1)
Gehan's Test
437(1)
Power and Sample Size
438(2)
Summary
440(1)
Problems
440(4)
What Do the Data Really Show?
444(22)
When to Use Which Test
445(2)
Randomize and Control
447(8)
Internal Mammary Artery Ligation to Treat Angina Pectoris
448(1)
The Portacaval Shunt to Treat Cirrhosis of the Liver
449(3)
Is Randomization of People Ethical?
452(2)
Is a Randomized Controlled Trial Always Necessary?
454(1)
Does Randomization Ensure Correct Conclusions?
455(5)
Problems with the Population
460(2)
How You Can Improve Things
462(4)
Appendix A Computational Forms466(6)
Appendix B Power Charts472(9)
Appendix C Answers to Exercises481(16)
Index497
Stanton A. Glantz, PhD: Professor of Medicine, also Member, Cardiovascular Research Institute, Institute for Health Policy Studies, and Cancer Center, University of California, San Francisco.

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